from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()
We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup
time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.
KNeighborsClassifier_brute_force¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=brute.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | e70d8e7886766d41f30889506baa1e67 | 2cf49829391aa7891e877f2ed070adf0 | 2.037486 | 0.160038 | NaN | 0.000393 | 0.002037 | brute | -1 | 1 | 0.663 | 0.185754 | 0.003161 | 0.687 | 10.968758 | 10.970346 | 0.024 |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 22d5eac4a4146149b35eae6914921514 | 2cf49829391aa7891e877f2ed070adf0 | 2.840747 | 0.033275 | NaN | 0.000282 | 0.002841 | brute | -1 | 5 | 0.757 | 0.188271 | 0.001007 | 0.742 | 15.088581 | 15.088797 | 0.015 |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 2.063801 | 0.004240 | NaN | 0.000388 | 0.002064 | brute | 1 | 100 | 0.882 | 0.230377 | 0.007822 | 0.875 | 8.958368 | 8.963530 | 0.007 |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 16f94fc93942ff7ece860c9b0f64645f | 2cf49829391aa7891e877f2ed070adf0 | 0.019551 | 0.000110 | NaN | 0.000041 | 0.019551 | brute | 1 | 100 | 1.000 | 0.008942 | 0.000266 | 0.000 | 2.186471 | 2.187441 | 1.000 |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 2.858204 | 0.019720 | NaN | 0.000280 | 0.002858 | brute | -1 | 100 | 0.882 | 0.228269 | 0.001995 | 0.875 | 12.521228 | 12.521707 | 0.007 |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 2d5f2b2c02d77766434d9e5033b4a76c | 2cf49829391aa7891e877f2ed070adf0 | 0.024168 | 0.003516 | NaN | 0.000033 | 0.024168 | brute | -1 | 100 | 1.000 | 0.009336 | 0.001486 | 0.000 | 2.588690 | 2.621270 | 1.000 |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | efac583623711d395ddae7a1e881ff68 | 2cf49829391aa7891e877f2ed070adf0 | 2.064481 | 0.028765 | NaN | 0.000388 | 0.002064 | brute | 1 | 5 | 0.757 | 0.188912 | 0.002451 | 0.742 | 10.928284 | 10.929203 | 0.015 |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | b79d63bb7670c492de5c3befac58fe29 | 2cf49829391aa7891e877f2ed070adf0 | 1.183130 | 0.005814 | NaN | 0.000676 | 0.001183 | brute | 1 | 1 | 0.663 | 0.187264 | 0.002336 | 0.687 | 6.317962 | 6.318454 | 0.024 |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | e70d8e7886766d41f30889506baa1e67 | 88843f54689e3271092f70126e1de585 | 1.815722 | 0.028080 | NaN | 0.000009 | 0.001816 | brute | -1 | 1 | 0.896 | 0.028371 | 0.000520 | 0.967 | 63.999399 | 64.010166 | 0.071 |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 22d5eac4a4146149b35eae6914921514 | 88843f54689e3271092f70126e1de585 | 2.707666 | 0.022798 | NaN | 0.000006 | 0.002708 | brute | -1 | 5 | 0.922 | 0.029064 | 0.000476 | 0.974 | 93.160682 | 93.173190 | 0.052 |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 16f94fc93942ff7ece860c9b0f64645f | 88843f54689e3271092f70126e1de585 | 1.953524 | 0.001538 | NaN | 0.000008 | 0.001954 | brute | 1 | 100 | 0.929 | 0.066193 | 0.002342 | 0.975 | 29.512759 | 29.531232 | 0.046 |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2d5f2b2c02d77766434d9e5033b4a76c | 88843f54689e3271092f70126e1de585 | 2.741653 | 0.030596 | NaN | 0.000006 | 0.002742 | brute | -1 | 100 | 0.929 | 0.065436 | 0.001631 | 0.975 | 41.898437 | 41.911457 | 0.046 |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | efac583623711d395ddae7a1e881ff68 | 88843f54689e3271092f70126e1de585 | 1.942469 | 0.004236 | NaN | 0.000008 | 0.001942 | brute | 1 | 5 | 0.922 | 0.029181 | 0.000577 | 0.974 | 66.567041 | 66.580063 | 0.052 |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | b79d63bb7670c492de5c3befac58fe29 | 88843f54689e3271092f70126e1de585 | 1.070771 | 0.003015 | NaN | 0.000015 | 0.001071 | brute | 1 | 1 | 0.896 | 0.027912 | 0.000561 | 0.967 | 38.362752 | 38.370504 | 0.071 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.147 | 0.0 | -1 | 1 | 0.048 | 0.003 | 0.234 | 0.235 | See | See |
| 3 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.193 | 0.0 | -1 | 5 | 0.047 | 0.000 | 0.236 | 0.237 | See | See |
| 6 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.139 | 0.0 | 1 | 100 | 0.047 | 0.000 | 0.238 | 0.238 | See | See |
| 9 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.016 | 0.006 | 5.082 | 0.0 | -1 | 100 | 0.047 | 0.000 | 0.335 | 0.335 | See | See |
| 12 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.221 | 0.0 | 1 | 5 | 0.047 | 0.000 | 0.236 | 0.236 | See | See |
| 15 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 100 | 0.011 | 0.000 | 7.145 | 0.0 | 1 | 1 | 0.047 | 0.000 | 0.238 | 0.238 | See | See |
| 18 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.376 | 0.0 | -1 | 1 | 0.008 | 0.000 | 0.508 | 0.508 | See | See |
| 21 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.370 | 0.0 | -1 | 5 | 0.008 | 0.000 | 0.513 | 0.513 | See | See |
| 24 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.379 | 0.0 | 1 | 100 | 0.008 | 0.000 | 0.500 | 0.500 | See | See |
| 27 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.375 | 0.0 | -1 | 100 | 0.008 | 0.000 | 0.509 | 0.509 | See | See |
| 30 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.376 | 0.0 | 1 | 5 | 0.008 | 0.000 | 0.506 | 0.506 | See | See |
| 33 | KNeighborsClassifier_brute_force | fit | 100000 | 100000 | 2 | 0.004 | 0.000 | 0.375 | 0.0 | 1 | 1 | 0.008 | 0.000 | 0.506 | 0.507 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.037 | 0.160 | 0.000 | 0.002 | -1 | 1 | 0.186 | 0.003 | 10.969 | 10.970 | See | See |
| 2 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.021 | 0.001 | 0.000 | 0.021 | -1 | 1 | 0.009 | 0.000 | 2.401 | 2.401 | See | See |
| 4 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.841 | 0.033 | 0.000 | 0.003 | -1 | 5 | 0.188 | 0.001 | 15.089 | 15.089 | See | See |
| 5 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.023 | 0.001 | 0.000 | 0.023 | -1 | 5 | 0.009 | 0.000 | 2.591 | 2.591 | See | See |
| 7 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.064 | 0.004 | 0.000 | 0.002 | 1 | 100 | 0.230 | 0.008 | 8.958 | 8.964 | See | See |
| 8 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.020 | 0.000 | 0.000 | 0.020 | 1 | 100 | 0.009 | 0.000 | 2.186 | 2.187 | See | See |
| 10 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.858 | 0.020 | 0.000 | 0.003 | -1 | 100 | 0.228 | 0.002 | 12.521 | 12.522 | See | See |
| 11 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.024 | 0.004 | 0.000 | 0.024 | -1 | 100 | 0.009 | 0.001 | 2.589 | 2.621 | See | See |
| 13 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 2.064 | 0.029 | 0.000 | 0.002 | 1 | 5 | 0.189 | 0.002 | 10.928 | 10.929 | See | See |
| 14 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.020 | 0.000 | 0.000 | 0.020 | 1 | 5 | 0.009 | 0.000 | 2.190 | 2.191 | See | See |
| 16 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 100 | 1.183 | 0.006 | 0.001 | 0.001 | 1 | 1 | 0.187 | 0.002 | 6.318 | 6.318 | See | See |
| 17 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 100 | 0.018 | 0.000 | 0.000 | 0.018 | 1 | 1 | 0.009 | 0.000 | 2.057 | 2.057 | See | See |
| 19 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.816 | 0.028 | 0.000 | 0.002 | -1 | 1 | 0.028 | 0.001 | 63.999 | 64.010 | See | See |
| 20 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.009 | 0.005 | 0.000 | 0.009 | -1 | 1 | 0.001 | 0.000 | 12.520 | 12.593 | See | See |
| 22 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.708 | 0.023 | 0.000 | 0.003 | -1 | 5 | 0.029 | 0.000 | 93.161 | 93.173 | See | See |
| 23 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.006 | 0.002 | 0.000 | 0.006 | -1 | 5 | 0.001 | 0.000 | 7.676 | 7.699 | See | See |
| 25 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.954 | 0.002 | 0.000 | 0.002 | 1 | 100 | 0.066 | 0.002 | 29.513 | 29.531 | See | See |
| 26 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.795 | 3.810 | See | See |
| 28 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 2.742 | 0.031 | 0.000 | 0.003 | -1 | 100 | 0.065 | 0.002 | 41.898 | 41.911 | See | See |
| 29 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.007 | 0.005 | 0.000 | 0.007 | -1 | 100 | 0.001 | 0.000 | 8.877 | 8.942 | See | See |
| 31 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.942 | 0.004 | 0.000 | 0.002 | 1 | 5 | 0.029 | 0.001 | 66.567 | 66.580 | See | See |
| 32 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.003 | 0.000 | 0.000 | 0.003 | 1 | 5 | 0.001 | 0.000 | 4.136 | 4.158 | See | See |
| 34 | KNeighborsClassifier_brute_force | predict | 100000 | 1000 | 2 | 1.071 | 0.003 | 0.000 | 0.001 | 1 | 1 | 0.028 | 0.001 | 38.363 | 38.371 | See | See |
| 35 | KNeighborsClassifier_brute_force | predict | 100000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | 1 | 1 | 0.001 | 0.000 | 2.630 | 2.642 | See | See |
KNeighborsClassifier_kd_tree¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=kd_tree.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | n_jobs | n_neighbors | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | fe8267e25dadb302ed498e50fabf7ef4 | 1eb6cf1a720a225efb91df0b529b0510 | 0.795165 | 0.983172 | NaN | 0.000101 | 0.000795 | kd_tree | -1 | 1 | 0.929 | 0.123675 | 0.004460 | 0.910 | 6.429500 | 6.433679 | 0.019 |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | dc74b969bf622bc24ba3bc62c980983b | 1eb6cf1a720a225efb91df0b529b0510 | 1.050018 | 0.315225 | NaN | 0.000076 | 0.001050 | kd_tree | -1 | 5 | 0.946 | 0.240553 | 0.010176 | 0.941 | 4.365021 | 4.368925 | 0.005 |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 41accfcdcd5c50784bedf6164b63de99 | 1eb6cf1a720a225efb91df0b529b0510 | 5.603861 | 0.406256 | NaN | 0.000014 | 0.005604 | kd_tree | 1 | 100 | 0.951 | 0.704055 | 0.006573 | 0.940 | 7.959412 | 7.959759 | 0.011 |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 022f7445d43bb1dbc24dc3106c03cb93 | 1eb6cf1a720a225efb91df0b529b0510 | 3.127008 | 0.108438 | NaN | 0.000026 | 0.003127 | kd_tree | -1 | 100 | 0.951 | 0.676374 | 0.006511 | 0.940 | 4.623195 | 4.623409 | 0.011 |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 84622ba45e941db642965553529e1941 | 1eb6cf1a720a225efb91df0b529b0510 | 1.678828 | 0.200153 | NaN | 0.000048 | 0.001679 | kd_tree | 1 | 5 | 0.946 | 0.231535 | 0.003826 | 0.941 | 7.250853 | 7.251843 | 0.005 |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | a9455565db1d8e052a783317c99744ff | 1eb6cf1a720a225efb91df0b529b0510 | 0.956498 | 0.273766 | NaN | 0.000084 | 0.000956 | kd_tree | 1 | 1 | 0.929 | 0.124604 | 0.001311 | 0.910 | 7.676307 | 7.676732 | 0.019 |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | fe8267e25dadb302ed498e50fabf7ef4 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.023352 | 0.010450 | NaN | 0.000685 | 0.000023 | kd_tree | -1 | 1 | 0.891 | 0.000369 | 0.000080 | 0.879 | 63.332646 | 64.807040 | 0.012 |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | dc74b969bf622bc24ba3bc62c980983b | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.020542 | 0.000941 | NaN | 0.000779 | 0.000021 | kd_tree | -1 | 5 | 0.911 | 0.000613 | 0.000036 | 0.905 | 33.522498 | 33.581772 | 0.006 |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 41accfcdcd5c50784bedf6164b63de99 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.034418 | 0.005652 | NaN | 0.000465 | 0.000034 | kd_tree | 1 | 100 | 0.894 | 0.004355 | 0.000120 | 0.917 | 7.903084 | 7.906094 | 0.023 |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 022f7445d43bb1dbc24dc3106c03cb93 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.035195 | 0.005084 | NaN | 0.000455 | 0.000035 | kd_tree | -1 | 100 | 0.894 | 0.005130 | 0.001452 | 0.917 | 6.860791 | 7.130146 | 0.023 |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 84622ba45e941db642965553529e1941 | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.017327 | 0.000102 | NaN | 0.000923 | 0.000017 | kd_tree | 1 | 5 | 0.911 | 0.000610 | 0.000046 | 0.905 | 28.398379 | 28.477641 | 0.006 |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | a9455565db1d8e052a783317c99744ff | edf7cb014c2c0910e1c3aaaf8ae633f2 | 0.016274 | 0.000151 | NaN | 0.000983 | 0.000016 | kd_tree | 1 | 1 | 0.891 | 0.000396 | 0.000066 | 0.879 | 41.117383 | 41.684272 | 0.012 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.133 | 0.035 | 0.026 | 0.0 | -1 | 1 | 0.801 | 0.052 | 3.912 | 3.920 | See | See |
| 3 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.764 | 0.040 | 0.021 | 0.0 | -1 | 5 | 0.781 | 0.012 | 4.819 | 4.819 | See | See |
| 6 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.838 | 0.074 | 0.021 | 0.0 | 1 | 100 | 0.753 | 0.014 | 5.096 | 5.097 | See | See |
| 9 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.550 | 0.091 | 0.023 | 0.0 | -1 | 100 | 0.789 | 0.019 | 4.497 | 4.498 | See | See |
| 12 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.568 | 0.076 | 0.022 | 0.0 | 1 | 5 | 0.755 | 0.008 | 4.729 | 4.729 | See | See |
| 15 | KNeighborsClassifier_kd_tree | fit | 1000000 | 1000000 | 10 | 3.754 | 0.043 | 0.021 | 0.0 | 1 | 1 | 0.783 | 0.008 | 4.793 | 4.793 | See | See |
| 18 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.001 | 0.017 | 0.0 | -1 | 1 | 0.004 | 0.004 | 0.245 | 0.347 | See | See |
| 21 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.034 | 0.0 | -1 | 5 | 0.002 | 0.001 | 0.242 | 0.296 | See | See |
| 24 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | 1 | 100 | 0.001 | 0.000 | 0.552 | 0.555 | See | See |
| 27 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.001 | 0.000 | 0.031 | 0.0 | -1 | 100 | 0.001 | 0.000 | 0.596 | 0.597 | See | See |
| 30 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | 1 | 5 | 0.001 | 0.000 | 0.525 | 0.528 | See | See |
| 33 | KNeighborsClassifier_kd_tree | fit | 1000 | 1000 | 2 | 0.000 | 0.000 | 0.033 | 0.0 | 1 | 1 | 0.001 | 0.000 | 0.565 | 0.565 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | n_jobs | n_neighbors | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.795 | 0.983 | 0.000 | 0.001 | -1 | 1 | 0.124 | 0.004 | 6.430 | 6.434 | See | See |
| 2 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.000 | 0.000 | 0.003 | -1 | 1 | 0.000 | 0.000 | 9.258 | 9.569 | See | See |
| 4 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.050 | 0.315 | 0.000 | 0.001 | -1 | 5 | 0.241 | 0.010 | 4.365 | 4.369 | See | See |
| 5 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | -1 | 5 | 0.000 | 0.000 | 7.423 | 7.913 | See | See |
| 7 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 5.604 | 0.406 | 0.000 | 0.006 | 1 | 100 | 0.704 | 0.007 | 7.959 | 7.960 | See | See |
| 8 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.003 | 0.001 | 0.000 | 0.003 | 1 | 100 | 0.001 | 0.000 | 3.732 | 3.892 | See | See |
| 10 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 3.127 | 0.108 | 0.000 | 0.003 | -1 | 100 | 0.676 | 0.007 | 4.623 | 4.623 | See | See |
| 11 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.005 | 0.001 | 0.000 | 0.005 | -1 | 100 | 0.001 | 0.000 | 6.199 | 6.485 | See | See |
| 13 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 1.679 | 0.200 | 0.000 | 0.002 | 1 | 5 | 0.232 | 0.004 | 7.251 | 7.252 | See | See |
| 14 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 3.111 | 3.285 | See | See |
| 16 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1000 | 10 | 0.956 | 0.274 | 0.000 | 0.001 | 1 | 1 | 0.125 | 0.001 | 7.676 | 7.677 | See | See |
| 17 | KNeighborsClassifier_kd_tree | predict | 1000000 | 1 | 10 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 3.222 | 3.440 | See | See |
| 19 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.023 | 0.010 | 0.001 | 0.000 | -1 | 1 | 0.000 | 0.000 | 63.333 | 64.807 | See | See |
| 20 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 1 | 0.000 | 0.000 | 25.329 | 26.805 | See | See |
| 22 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.021 | 0.001 | 0.001 | 0.000 | -1 | 5 | 0.001 | 0.000 | 33.522 | 33.582 | See | See |
| 23 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 5 | 0.000 | 0.000 | 22.209 | 23.444 | See | See |
| 25 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.034 | 0.006 | 0.000 | 0.000 | 1 | 100 | 0.004 | 0.000 | 7.903 | 7.906 | See | See |
| 26 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 100 | 0.000 | 0.000 | 5.917 | 6.270 | See | See |
| 28 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.035 | 0.005 | 0.000 | 0.000 | -1 | 100 | 0.005 | 0.001 | 6.861 | 7.130 | See | See |
| 29 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.002 | 0.000 | 0.000 | 0.002 | -1 | 100 | 0.000 | 0.000 | 19.676 | 21.228 | See | See |
| 31 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.017 | 0.000 | 0.001 | 0.000 | 1 | 5 | 0.001 | 0.000 | 28.398 | 28.478 | See | See |
| 32 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 5 | 0.000 | 0.000 | 6.492 | 6.981 | See | See |
| 34 | KNeighborsClassifier_kd_tree | predict | 1000 | 1000 | 2 | 0.016 | 0.000 | 0.001 | 0.000 | 1 | 1 | 0.000 | 0.000 | 41.117 | 41.684 | See | See |
| 35 | KNeighborsClassifier_kd_tree | predict | 1000 | 1 | 2 | 0.001 | 0.000 | 0.000 | 0.001 | 1 | 1 | 0.000 | 0.000 | 5.968 | 6.407 | See | See |
KMeans_tall¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.518 | 0.060 | 30 | 0.031 | 0.0 | random | 0.410 | 0.029 | 1.262 | 1.266 | See | See |
| 3 | KMeans_tall | fit | 1000000 | 1000000 | 2 | 0.575 | 0.015 | 30 | 0.028 | 0.0 | k-means++ | 0.449 | 0.030 | 1.280 | 1.283 | See | See |
| 6 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 6.302 | 0.328 | 30 | 0.127 | 0.0 | random | 2.921 | 0.026 | 2.158 | 2.158 | See | See |
| 9 | KMeans_tall | fit | 1000000 | 1000000 | 100 | 6.368 | 0.051 | 30 | 0.126 | 0.0 | k-means++ | 3.090 | 0.028 | 2.061 | 2.061 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.001 | 0.000 | 30 | 0.011 | 0.000 | random | 0.0 | 0.0 | 8.910 | 12.415 | See | See |
| 2 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | random | 0.0 | 0.0 | 8.750 | 12.945 | See | See |
| 4 | KMeans_tall | predict | 1000000 | 1000 | 2 | 0.002 | 0.001 | 30 | 0.010 | 0.000 | k-means++ | 0.0 | 0.0 | 11.034 | 12.328 | See | See |
| 5 | KMeans_tall | predict | 1000000 | 1 | 2 | 0.001 | 0.000 | 30 | 0.000 | 0.001 | k-means++ | 0.0 | 0.0 | 12.111 | 13.303 | See | See |
| 7 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.476 | 0.000 | random | 0.0 | 0.0 | 5.981 | 6.407 | See | See |
| 8 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | random | 0.0 | 0.0 | 12.333 | 12.636 | See | See |
| 10 | KMeans_tall | predict | 1000000 | 1000 | 100 | 0.002 | 0.000 | 30 | 0.493 | 0.000 | k-means++ | 0.0 | 0.0 | 6.014 | 6.397 | See | See |
| 11 | KMeans_tall | predict | 1000000 | 1 | 100 | 0.001 | 0.000 | 30 | 0.001 | 0.001 | k-means++ | 0.0 | 0.0 | 11.200 | 11.497 | See | See |
KMeans_short¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | algorithm | init | max_iter | n_clusters | n_init | tol | adjusted_rand_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | adjusted_rand_score_sklearnex | speedup | std_speedup | diff_adjusted_rand_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 3db9c030f488d0e8ff350ae449da4627 | 058b7e3842b587a5c675518f0706f5ee | 0.001728 | 0.000094 | 20 | 0.009258 | 0.000002 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.000126 | 0.000507 | 0.000038 | -0.000965 | 3.411913 | 3.421573 | 0.001090 |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | ac9b8cec4749455c0d1cd1a92c150716 | 058b7e3842b587a5c675518f0706f5ee | 0.001752 | 0.000117 | 20 | 0.009132 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.001245 | 0.000497 | 0.000035 | -0.000750 | 3.524897 | 3.533599 | 0.001995 |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 3db9c030f488d0e8ff350ae449da4627 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002619 | 0.000256 | 20 | 0.305427 | 0.000003 | full | random | 20 | 300 | 1 | 1.000000e-16 | 0.278733 | 0.001042 | 0.000099 | 0.293767 | 2.512904 | 2.524307 | 0.015034 |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | ac9b8cec4749455c0d1cd1a92c150716 | 7f85b913f395ec54101a6738ea63a9a7 | 0.002463 | 0.000166 | 20 | 0.324861 | 0.000002 | full | k-means++ | 20 | 300 | 1 | 1.000000e-16 | 0.317011 | 0.001089 | 0.000093 | 0.256968 | 2.260661 | 2.268859 | 0.060044 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | KMeans_short | fit | 10000 | 10000 | 2 | 0.082 | 0.001 | 20 | 0.002 | 0.0 | random | 0.030 | 0.002 | 2.725 | 2.729 | See | See |
| 3 | KMeans_short | fit | 10000 | 10000 | 2 | 0.239 | 0.003 | 20 | 0.001 | 0.0 | k-means++ | 0.082 | 0.001 | 2.902 | 2.903 | See | See |
| 6 | KMeans_short | fit | 10000 | 10000 | 100 | 0.225 | 0.005 | 20 | 0.036 | 0.0 | random | 0.124 | 0.002 | 1.815 | 1.816 | See | See |
| 9 | KMeans_short | fit | 10000 | 10000 | 100 | 0.619 | 0.011 | 20 | 0.013 | 0.0 | k-means++ | 0.324 | 0.008 | 1.908 | 1.909 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | init | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | random | 0.001 | 0.0 | 3.412 | 3.422 | See | See |
| 2 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | random | 0.000 | 0.0 | 13.473 | 13.814 | See | See |
| 4 | KMeans_short | predict | 10000 | 1000 | 2 | 0.002 | 0.0 | 20 | 0.009 | 0.000 | k-means++ | 0.000 | 0.0 | 3.525 | 3.534 | See | See |
| 5 | KMeans_short | predict | 10000 | 1 | 2 | 0.001 | 0.0 | 20 | 0.000 | 0.001 | k-means++ | 0.000 | 0.0 | 12.802 | 13.215 | See | See |
| 7 | KMeans_short | predict | 10000 | 1000 | 100 | 0.003 | 0.0 | 20 | 0.305 | 0.000 | random | 0.001 | 0.0 | 2.513 | 2.524 | See | See |
| 8 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | random | 0.000 | 0.0 | 8.803 | 8.912 | See | See |
| 10 | KMeans_short | predict | 10000 | 1000 | 100 | 0.002 | 0.0 | 20 | 0.325 | 0.000 | k-means++ | 0.001 | 0.0 | 2.261 | 2.269 | See | See |
| 11 | KMeans_short | predict | 10000 | 1 | 100 | 0.001 | 0.0 | 20 | 0.001 | 0.001 | k-means++ | 0.000 | 0.0 | 9.020 | 9.214 | See | See |
LogisticRegression¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | C | class_weight | dual | fit_intercept | intercept_scaling | l1_ratio | max_iter | multi_class | n_jobs | penalty | random_state | solver | tol | verbose | warm_start | accuracy_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | accuracy_score_sklearnex | speedup | std_speedup | diff_accuracy_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | f11be2529b0a77bc8fb5ee8de141e6b2 | 09c36ed6521e03974d0c134895f56c01 | 0.000373 | 0.000329 | [20] | 2.147105 | 3.725947e-07 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.56 | 0.001442 | 0.003395 | 0.55 | 0.258418 | 0.661014 | 0.01 |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | f11be2529b0a77bc8fb5ee8de141e6b2 | 7daecaea01e5baa8c862f264e3c1c3b0 | 0.001828 | 0.000343 | [26] | 4.375858 | 1.828213e-05 | 1.0 | NaN | False | True | 1 | NaN | 100 | auto | NaN | l2 | NaN | lbfgs | 0.0001 | 0 | False | 0.35 | 0.004849 | 0.000794 | 0.28 | 0.377011 | 0.382034 | 0.07 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | LogisticRegression | fit | 1000000 | 1000000 | 100 | 11.279 | 0.386 | [20] | 0.071 | 0.000 | 2.053 | 0.031 | 5.495 | 5.495 | See | See |
| 3 | LogisticRegression | fit | 1000 | 1000 | 10000 | 0.957 | 0.498 | [26] | 0.084 | 0.001 | 0.990 | 0.036 | 0.967 | 0.968 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | LogisticRegression | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | [20] | 2.147 | 0.0 | 0.001 | 0.003 | 0.258 | 0.661 | See | See |
| 2 | LogisticRegression | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | [20] | 0.014 | 0.0 | 0.000 | 0.000 | 0.423 | 0.431 | See | See |
| 4 | LogisticRegression | predict | 1000 | 100 | 10000 | 0.002 | 0.0 | [26] | 4.376 | 0.0 | 0.005 | 0.001 | 0.377 | 0.382 | See | See |
| 5 | LogisticRegression | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | [26] | 1.042 | 0.0 | 0.001 | 0.000 | 0.053 | 0.053 | See | See |
Ridge¶scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)
All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.
| estimator | function | n_samples_train | n_samples | n_features | parameters_digest | dataset_digest | mean_duration_sklearn | std_duration_sklearn | n_iter | iteration_throughput | latency | alpha | copy_X | fit_intercept | max_iter | normalize | random_state | solver | tol | r2_score_sklearn | mean_duration_sklearnex | std_duration_sklearnex | r2_score_sklearnex | speedup | std_speedup | diff_r2_scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | a70ae8dc5c059ec88cf27c61016aee2a | 14fae294d530397144c65ba3209bf125 | 0.011756 | 0.000117 | NaN | 6.805114 | 0.000012 | 1.0 | True | True | NaN | deprecated | NaN | auto | 0.001 | 0.082567 | 0.018804 | 0.000152 | 0.122191 | 0.625195 | 0.625216 | 0.039624 |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Ridge | fit | 1000 | 1000 | 10000 | 0.186 | 0.004 | 0.430 | 0.0 | 0.187 | 0.002 | 0.993 | 0.993 | See | See |
| 3 | Ridge | fit | 1000000 | 1000000 | 100 | 1.129 | 0.048 | 0.708 | 0.0 | 0.315 | 0.255 | 3.586 | 4.614 | See | See |
| estimator | function | n_samples_train | n_samples | n_features | mean_duration_sklearn | std_duration_sklearn | iteration_throughput | latency | mean_duration_sklearnex | std_duration_sklearnex | speedup | std_speedup | sklearn_profiling | sklearnex_profiling | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Ridge | predict | 1000 | 1000 | 10000 | 0.012 | 0.0 | 6.805 | 0.0 | 0.019 | 0.0 | 0.625 | 0.625 | See | See |
| 2 | Ridge | predict | 1000 | 1 | 10000 | 0.000 | 0.0 | 1.216 | 0.0 | 0.000 | 0.0 | 0.797 | 0.879 | See | See |
| 4 | Ridge | predict | 1000000 | 1000 | 100 | 0.000 | 0.0 | 5.766 | 0.0 | 0.000 | 0.0 | 0.431 | 0.623 | See | See |
| 5 | Ridge | predict | 1000000 | 1 | 100 | 0.000 | 0.0 | 0.019 | 0.0 | 0.000 | 0.0 | 0.581 | 0.618 | See | See |